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NVIDIA NCP-AII Prüfungsplan:

Thema	Einzelheiten
Thema 1	System and Server Bring-up: Covers end-to-end physical setup of GPU-based AI infrastructure, including BMC OOB TPM configuration, firmware upgrades, hardware installation, and power and cooling validation to ensure servers are workload-ready.
Thema 2	Control Plane Installation and Configuration: Covers deploying the software stack including Base Command Manager, OS, Slurm Enroot Pyxis, NVIDIA GPU and DOCA drivers, container toolkit, and NGC CLI.
Thema 3	Cluster Test and Verification: Covers full cluster validation through HPL and NCCL benchmarks, NVLink and fabric bandwidth tests, cable and firmware checks, and burn-in testing using HPL, NCCL, and NeMo.
Thema 4	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Thema 5	Troubleshoot and Optimize: Covers identifying and replacing faulty hardware components such as GPUs, network cards, and power supplies, along with performance optimization for AMD Intel servers and storage.

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NVIDIA AI Infrastructure NCP-AII Prüfungsfragen mit Lösungen (Q154-Q159):

154. Frage
A user encounters "permission denied" errors when running GPU-accelerated containers on a Secure Boot- enabled system. What resolves this?

A. Run Docker with sudo for elevated privileges.
B. Disable SELinux to relax unnecessary security policies.
C. Reinstall Docker without the NVIDIA runtime.
D. Enroll the MOK and sign NVIDIA kernel modules.

Antwort: D

Begründung:
On systems where UEFI Secure Boot is enabled, the Linux kernel strictly enforces a "Signature Verification" policy for all loaded kernel modules. The NVIDIA driver consists of several modules (like nvidia.ko and nvidia-uvm.ko) that provide the interface between the hardware and the NVIDIA Container Runtime. If these modules are not signed by a key trusted by the system's firmware, the kernel will block them from loading, leading to "Permission Denied" errors when the container attempts to access /dev/nvidiactl or the GPU device nodes. To resolve this without compromising the security of the host, the administrator must utilize aMachine Owner Key (MOK). The modules must be signed using this key, and the key itself must be "Enrolled" into the system's Secure Boot database via the MOK management interface during the boot process. This establishes a "Chain of Trust" that allows the drivers to function. Disabling SELinux (Option C) or using sudo (Option D) will not resolve this, as the blockage is happening at the kernel-firmware interface level, not at the user-space permission level.

155. Frage
You are implementing a security policy on a BlueField-2 DPU to filter traffic based on specific application signatures. Which technology, supported by BlueField, allows you to achieve deep packet inspection (DPI) and apply security rules based on the detected application?

A. TC (Traffic Control) with 'iptables' rules.
B. OVS (Open vSwitch) with OpenFlow rules.
C. Netfilter with connection tracking.
D. eBPF (extended Berkeley Packet Filter) with XDP (eXpress Data Path).
E. IPsec (Internet Protocol Security) tunnels.

Antwort: D

Begründung:
eBPF with XDP is the most suitable technology for deep packet inspection (DPI) on BlueField. It allows you to run custom code at near-line speed to inspect packets and apply security rules based on application signatures. TC and Netfilter are less efficient for DPI, OVS/OpenFlow are more for switching policies, and IPsec focuses on encryption.

156. Frage
What is the best practice for configuring memory in an NVIDIA certified server for optimizing performance?

A. Populate an odd number of DIMMs per CPU socket.
B. Use different DIMM sizes across memory channels
C. Populate all memory channels evenly with identical DIMMs
D. Leave half of the available memory channels unpopulated

Antwort: C

Begründung:
NVIDIA certified servers should have memory channels populated evenly with identical DIMMs to maximize memory bandwidth and maintain balanced CPU memory access. This avoids channel imbalance and helps deliver consistent performance for GPU-accelerated workloads.

157. Frage
You are configuring a BlueField DPU to run a custom packet processing application. You want to ensure that the application has exclusive access to certain CPU cores on the DPU. Which mechanism is best suited for isolating CPU cores for your application on the Bluefield DPU?

A. Utilizing cgroups (control groups) to create a dedicated cgroup for the application and limit its CPU usage to specific cores.
B. Using 'taskset' command to pin the application's processes to specific cores.
C. Adjusting the kernel's scheduler parameters to prioritize the application's threads on the desired cores.
D. Using CPIJ affinity settings within the application code itself.
E. Modifying the DPU's bootloader configuration to disable the cores you want to reserve.

Antwort: A

Begründung:
Cgroups provide a robust and flexible way to isolate and manage resources, including CPU cores, for applications. They allow you to create a dedicated cgroup for your application and limit its CPU usage to specific cores. 'taskset' is a viable option, but cgroups offer more comprehensive resource management capabilities. Modifying the bootloader is not a practical or recommended approach. CPU affinity settings in the application code depend on the application's design and may not be as reliable. Adjusting kernel scheduler parameters can be complex and affect other processes.

158. Frage
You have a deep learning application that requires a specific version of the CUDA toolkit inside the container. How should you best ensure that the correct CUDA version is available within the container, considering the NVIDIA Container Toolkit is installed on the host?

A. Manually copy the necessary CUDA libraries from the host into the container using 'docker cp' before running the application.
B. Install the required CUDA toolkit version directly on the host operating system. The NVIDIA Container Toolkit will automatically map it into the container.
C. Specify the desired CUDA version when running the container using the '-env flag. The NVIDIA Container Toolkit will dynamically install the CUDA version during container startup.
D. Use a base image (e.g., from NVIDIA NGC) that already includes the desired CUDA toolkit version. This approach provides a consistent and reproducible environment.
E. Use the nvidia-container-cli to modify the existing image to install the proper cuda version.

Antwort: D

Begründung:
The recommended approach is to use a base image that already contains the desired CUDA version. NVIDIA provides pre-built images on NGC (NVIDIA GPU Cloud) that are specifically designed for deep learning and include the appropriate CUDA versions and other dependencies. Installing CUDA on the host and expecting it to be magically mapped (A) is not reliable. The NVIDIA Container Toolkit doesn't install CUDA on the fly (B). Manually copying libraries (D) is error-prone and doesn't handle dependencies well. While technically possible, using nvidia- container-cli to modify the image is more complex than using a base image.

159. Frage
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